Transformers are the key component in power system transmission and distribution networks. Condition-based maintenance will increase their expected life, and online monitoring is essential to ensure operation reliability. In this paper, a new approach to transformer online monitoring is provided based on partial discharge (PD) measurement. Simultaneous measurements of PD using IEC60270 and radio frequency (RF) techniques are employed to explore new features that can be used to distinguish between internal PDs and external interference, as well as among different internal PD sources. Stream clustering based on the density grid method with only a few features required is used to categorize active sources without requiring large storage of information. Active source identification is performed using feature extraction from micro-phase-resolved PD images with the histogram of oriented gradient method and a pre-trained network. Two experimental case studies are conducted to evaluate the effectiveness of the proposed method, one to demonstrate the distinction between internal and external sources and the other to demonstrate the distinction between different internal PD sources. It is shown that the proposed method for online monitoring of transformers using simultaneous measurement of PD with IEC60270 and RF techniques and stream clustering is very effective in distinguishing and identifying various PD sources without the requirement of large storage of data.